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Digital transformation in the public sector has encouraged the adoption of Artificial Intelligence (AI) as a strategic instrument to enhance the effectiveness and quality of public service delivery. In Indonesia, the implementation of AI within the public administrative system remains at an early stage and faces various structural, regulatory, and ethical challenges. This study aims to analyze the opportunities, challenges, and ethical implications of AI implementation in Indonesia’s public administration. The research employs a qualitative approach through literature review and policy analysis of governmental digital transformation regulations. The findings indicate that AI holds significant potential to improve bureaucratic efficiency, service transparency, and data-driven decision-making processes. However, regulatory gaps, limited digital literacy among public officials, the risk of algorithmic bias, and data protection concerns constitute major obstacles to its effective implementation. The novelty of this study lies in integrating public administration management analysis with a public service ethics framework grounded in good governance principles within the context of AI implementation in Indonesia. This study recommends strengthening regulatory frameworks for AI in the public sector, enhancing human resource capacity, and developing ethical guidelines for AI utilization to ensure that public services remain accountable, equitable, and oriented toward the public interest.

The development of artificial intelligence technology is a great opportunity for the fashion industry, especially in designers based on personalization and consumer needs. This study aims to examine Midjourney's AI technology in the design personalization process by integrating solid data and consumer style preferences. This research is expected to support the concept of mass customization in the fashion industry and increase the relevance of design to user character. This research uses a mixed method method by combining quantitative data and qualitative data. The research stages include body data collection and style preferences, prompt formulation, data-driven prompt formulation, design generation using Midjourney, design validation by experts and consumers, and integrated data analysis.The results showed that the majority of the designs produced were considered feasible in terms of construction (83%) and in accordance with the character of the consumer's body (75%). The modest and minimalist style categories received the highest personalization scores. The qualitative findings reinforce the quantitative results, showing that consumers feel the fit of the style and proportions of the design with the character of their bodies.The study concludes that Midjourney's AI integration in the fashion design process is able to effectively support design personalization, although it still requires the role of designers in technical refinement. This approach has the potential to be an innovative solution in the development of data-driven fashion design.

The rapid development of Artificial Intelligence (AI) technology has opened significant opportunities to support maritime monitoring systems, particularly in detecting anomalies in ship movements that may indicate illegal or abnormal activities. However, the understanding and utilization of this technology among the general public and maritime stakeholders remain limited. This Community Service Program aims to conduct socialization and dissemination of knowledge on AI-based ship anomaly detection through the development and utilization of an interactive and informative web-based socialization platform. This activity is the result of collaboration between a team of lecturers from the Faculty of Communication and Informatics Technology (FTKI) and the National Research and Innovation Agency (BRIN). The implementation methods include the design of web-based educational content, presentation of fundamental AI concepts and ship anomaly detection, as well as visual simulations of ship movement data analysis results. The web-based socialization platform serves as an educational medium to enhance users’ understanding of the benefits, working mechanisms, and potential applications of AI technology in maritime surveillance. The results indicate an improvement in participants’ understanding of ship anomaly detection concepts and the role of artificial intelligence in supporting maritime security and safety. This PKM activity is expected to promote technological literacy, strengthen synergy between academia and research institutions, and serve as a model for practical and sustainable web-based technology socialization

This study aims to analyze the transformative impact of digitalization and technology in the manufacturing sector on improving operational efficiency, particularly in budgeting and resource utilization, as well as to identify the main barriers to technology adoption. Using a Literature Review and Case Study Analysis of secondary data (journals, company reports, and industry publications), it was found that digitalization and Automation supported by Artificial Intelligence (AI) fundamentally transform budgeting functions. This transformation has been shown to improve budget accuracy by up to 50% (reducing human errors) and process efficiency by up to 25%, turning budgets from static documents into adaptive and predictive control tools. Positive impacts are also observed in operations through increased production capacity (revenue surge) and the implementation of Predictive Maintenance, which reduces expenditure and asset downtime, in line with the principles Cost Efficiency and Lean Manufacturing. Nevertheless, the adoption of advanced technology faces significant obstacles, namely high initial capital investment and skill gaps among the workforce. It is concluded that the success of digitalization heavily depends on strategic budget planning to overcome capital barriers and adequate allocation of funds for Human Resource (HR) training to support effective collaboration between humans and machines.

This research investigates the integration of Smart Production Systems (SPS) within the framework of Industry 5.0, emphasizing how such integration redefines operational efficiency and human–machine collaboration. The study aims to identify the contributions of smart technologies to productivity, sustainability, and human value in modern production systems. A Systematic Literature Review (SLR) was conducted following PRISMA guidelines, drawing from international databases including Elsevier, Springer, IEEE Xplore, Wiley, Taylor & Francis, ACM, and SAGE, as well as national sources. Publications from 2023–2025 were screened using keywords such as “Industry 5.0,” “Smart Production Systems,” “Human–Machine Collaboration,” and “Operational Efficiency.” Thematic analysis categorized findings into four dimensions: operational efficiency, human–machine collaboration, industrial sustainability, and socio-ethical aspects. Results indicate that SPS integration significantly enhances operational efficiency while fostering adaptive and creative collaboration between humans and machines. The combination of Artificial Intelligence (AI), Cyber-Physical Systems (CPS), and human creativity establishes a resilient, sustainable, and innovative production paradigm. Successful implementation of Industry 5.0 requires harmonizing technological advancement, human skills, and ethical principles. Practically, the study offers insights for industry stakeholders and policymakers in designing human-centered digital transformation strategies, strengthening supply chain resilience, workplace safety, and innovation. This research contributes conceptually by highlighting ethical and sustainable human–machine interactions in future production systems.

Rapid developments in the Artificial Intelligence (AI) industry have triggered an increased need for workers with specialized competencies, which has implications for significant variations in salary levels. This research aims to analyze the factors that influence salaries in the AI sector using the multiple linear regression method. The dataset used includes 15,000 AI job vacancies with variables including job and company characteristics. The data was engineered via the one-hot encoding method and divided into two parts: training data (80%) and test data (20%). The analysis results show that the regression model is able to explain 85% of the variation in salary, with an R² value of 0.85 and a Root Mean Square Error (RMSE) of USD 23,221. The three main factors identified as having a significant influence on salaries in the AI field are work experience, company location, and the industry in which the company operates. The experience factor reflects the skills and knowledge developed over many years, which can increase productivity (Rony et al., 2023). Company location also plays an important role, as the cost of living and demand for skilled labor varies by region (Badran, 2019). Additionally, the specific industry in which an employee works influences salary, given that more developed industries can often offer higher compensation (Huang, 2025). This research makes a significant empirical contribution to the understanding of compensation structures in the AI labor market.

The rapid development of generative Artificial Intelligence (AI) technology offers significant efficiency opportunities across various sectors; however, empirical research within the local Indonesian context, particularly in Batam City, remains scarce. This exploratory study aims to analyze the influence of positive perception, negative perception, and AI regulation understanding on technology adoption trends, as well as productivity and product innovation opportunities among creative industry players. The creative industry is a focal point due to the inherent tension between technological efficiency and the preservation of human originality in the creative process. This research employs a quantitative causal approach, utilizing a sample of 30 respondents reached through snowball sampling techniques. Data analysis was performed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS software. The findings reveal that only positive perception has a significant direct impact on product innovation (p=0.004), while AI regulation and technical adoption do not show significant effects. This suggests that a positive technology mindset is a more decisive factor for innovation than technical mastery or current regulatory compliance. These results underscore the urgent need for local government socialization of AI policies and enhanced digital literacy for Batam's creative practitioners to transform them from mere users into strategic AI-driven innovators.

This study aims to explore the condition of information technology infrastructure and to evaluate the extent to which the Lapak Aduan Banyumas service, managed by the Department of Communication and Informatics of Banyumas Regency, operates effectively. The study employs SWOT and Value Chain analysis approaches. A qualitative descriptive method is applied, with data collected through interviews and documentation involving officers responsible for managing the service. The results indicate that Lapak Aduan Banyumas has been operating optimally as a digital-based, transparent, and effective public complaint channel. Its main strengths lie in the ease of access through multiple service channels and the availability of features that enable real-time complaint status tracking. However, several challenges remain, including a limited number of human resources, less optimal collaboration among regional government organizations (OPDs), and network infrastructure constraints in several areas. The Value Chain analysis reveals that the complaint follow-up process and the dissemination of handling outcomes to the public represent the stages that generate the greatest added value in the service process. Therefore, this study suggests strengthening the complaint status monitoring system, enhancing inter-platform service integration, and utilizing artificial intelligence technologies to improve the complaint handling process. The findings of this study are expected to serve as a strategic basis for improving the quality of digital public services in Banyumas Regency.

Creative technology is the result of the convergence between human creativity and digital innovation that significantly changes the way industries operate. This journal discusses the definition of creative technology, its important role in supporting the development of the digital economy, and the challenges that arise in the implementation process. The research methodology uses a literature study of the latest technological trends, such as artificial intelligence (AI), augmented reality/virtual reality (AR/VR), and digital design. The results of the study show that the integration between technology and creativity is a key factor in increasing competitiveness in the future. Creative technology not only functions as a means of production, but also as a medium of innovation that is able to create added value, expand business opportunities, and encourage digital transformation in various sectors. However, the challenges faced include limited human resources, digital infrastructure readiness, and ethical and regulatory issues. Therefore, creative technology development strategies need to be directed at increasing digital literacy, cross-sector collaboration, and policies that support sustainable innovation.

This research aims to critically examine the ethical integration of artificial intelligence (AI) in education through the perspective of maqāṣid al-sharīʿah, emphasizing the alignment between technological innovation and Islamic moral principles. The methods used are a systematic literature review and thematic content analysis against peer-reviewed publications for the period 2015–2025, which discuss the application of AI in primary, secondary, and higher education. The study identified dominant ethical issues, such as data privacy, algorithmic bias, accountability, human agency, and moral development, which were then mapped to Islamic ethical goals, including ʿadl (justice), amānah (belief), karāmah al-insān (human dignity), and ḥifẓ al-ʿaql (protection of reason). The results of the analysis show that the adoption of AI in education often emphasizes efficiency, personalization, and predictive analytics, but has the potential to reduce learners' autonomy and ethical reasoning. The mapping of maqāṣid al-sharīʿah shows a strong normative conformity, so that Islamic principles can be a moral foundation as well as a practical guide for AI governance. The research contribution is theoretical by bridging the literature on AI ethics and Islamic educational philosophy, as well as practical by offering an integrative framework for AI policymakers, educators, and developers. The integration of maqāṣid al-sharīʿah in AI governance ensures justice, trust, inclusivity, and the development of the whole human being (insān kāmil).

Mathematics is a core subject that develops critical thinking skills; however, many third to fifth-grade elementary school students face difficulties with conventional teaching methods that tend to be uniform and less adaptive. This study aims to develop and implement a mobile-based educational game, "Ethno Run," which integrates the Bayesian Knowledge Tracing (BKT) algorithm to provide an adaptive learning experience. The method used is Research and Development (R&D) with the Multimedia Development Life Cycle (MDLC) framework. The system uses BKT to track students' mastery in real-time by analyzing their responses to pre-tests and exercises within the game, which then adjusts the difficulty level and focuses the post-test on areas identified as weak, such as arithmetic operations and geometry. The findings show that this adaptive approach significantly improves learning outcomes, with the average score increasing from 44.33 on the pre-test to 85.33 on the post-test among 30 students. This study concludes that the integration of Artificial Intelligence through BKT effectively personalizes learning, enhances student motivation, and provides data-driven insights for teachers regarding students' progress. The implication of this research is that adaptive game-based learning serves as a feasible interactive solution to bridge the gap in conventional basic mathematics education.

This research was designed not only to measure the direct impact of Artificial Intelligence (AI)-based digital teaching materials on motivation and learning outcomes but also to identify the factors influencing the effectiveness of their implementation in the context of a high school in an island area. The objectives of this study are: (1) To determine the significant effect of using Artificial Intelligence (AI)-based digital teaching materials on the learning motivation of Class XII students at SMAN 1 Kepulauan Selayar. And (2) To determine the significant effect of using Artificial Intelligence (AI)-based digital teaching materials on the learning outcomes of Class XII students at SMAN 1 Kepulauan Selayar. This study uses a quantitative approach through an experimental design to test the hypothesis regarding the significant effect of using Artificial Intelligence (AI)-based digital teaching materials on student motivation and learning outcomes in Economics. A sample of 30 Class XII students will be randomly selected. Data collection techniques include Questionnaires, Tests, observation, and documentation. The results of this study indicate (1) A significant and positive effect of the use of Artificial Intelligence (AI)-based digital teaching materials on the learning motivation of Class XII students at SMAN 1 Kepulauan Selayar. (2) The use of Artificial Intelligence (AI)-based digital teaching materials (X) significantly influences Economics learning outcomes (Y2).

The rapid advancement of artificial intelligence (AI) in the digital age offers substantial benefits by enhancing efficiency and productivity. Nevertheless, these developments also pose significant challenges to the protection of human rights. Issues such as privacy violations, algorithmic bias, discrimination, and opaque automated decision-making highlight the need for a strong integration of ethical values and legal frameworks in the use of AI. This study applies a normative legal method supported by literature-based research to examine the existing regulatory frameworks and the ethical principles underpinning them. The findings indicate that ethical principles such as transparency, accountability, fairness, and human-centeredness serve as essential moral guidelines to prevent AI misuse. Meanwhile, legal rules ensure certainty, establish accountability mechanisms, and provide sanctions for violations. The synergy between ethics and law forms a crucial foundation to ensure that technological innovation aligns with the protection of human rights, upholds human dignity, and supports the creation of a safe and just digital environment

Autonomous decision-making systems increasingly rely on complex artificial intelligence models to operate in dynamic and safety-critical environments. While these models provide strong predictive capabilities, their black-box nature limits transparency, trust, and accountability. This study proposes a structured research methodology for integrating Explainable Artificial Intelligence (XAI) into autonomous decision-making systems. The research adopts a conceptual–analytical approach to develop an explainability-oriented framework that embeds transparency across perception, decision-making, and action execution stages. The methodology includes literature-driven problem identification, conceptual framework construction, classification and mapping of XAI methods, and formulation of explainability evaluation criteria. The results demonstrate that effective explainability in autonomous systems requires a hybrid integration strategy, combining in-model transparency with post-hoc explanation mechanisms. A structured mapping of XAI techniques to autonomous system components and a conceptual decision-flow diagram are presented to illustrate explainability integration. The findings highlight that layered and context-aware explainability enhances system interpretability, supports human oversight, and improves safety relevance without compromising autonomous operation. This study contributes a reusable methodological foundation for the design and evaluation of explainable autonomous systems, offering practical guidance for future empirical validation and real-world deployment in safety-critical applications.

This research explores the impact of Cache Aware optimizations on signal processing pipelines in High Throughput computing systems. The growing demand for efficient memory management in modern computing systems, especially for data-intensive applications such as artificial intelligence (AI) and multimedia processing, necessitates the development of optimized memory hierarchies. Traditional memory systems often suffer from memory bottlenecks, significantly reducing the performance of these systems. This study investigates how memory hierarchy optimizations, particularly cache line aware optimization, dependency-aware caching, and adaptive cache replacement algorithms, can mitigate these challenges and improve system performance. Through analytical modeling and experimental benchmarking, this work evaluates various memory hierarchy configurations, including processing-in-memory (PIM) and three-dimensional integrated circuits (3D ICs), comparing them to conventional systems. The results demonstrate that Cache Aware optimizations lead to a reduction in memory access latency by up to 30%, while throughput improved by up to 40%. Additionally, cache hit rates increased by 25%, and energy consumption was reduced by up to 20%, highlighting the effectiveness of optimized memory management. The research contributes to the field by providing valuable insights into the design and implementation of efficient signal processing pipelines. It also identifies key challenges, including the need for dynamic occupancy mechanisms and DAG-aware scheduling algorithms, and suggests potential areas for future research, such as the exploration of collaborative caching approaches and further optimization of cache-adaptive algorithms. This work lays the foundation for more efficient, high-performance computing systems that can handle large datasets and complex tasks in real-time applications.

This study explores the role of visual analytics in enhancing decision-making processes within creative industries, focusing on its application to large-scale multimedia datasets. Visual analytics integrates interactive visualization techniques with computational algorithms, enabling users to explore complex datasets intuitively and derive actionable insights. The research centers on the design and implementation of interactive dashboards tailored to the creative sector, particularly film, music, and advertising industries, to facilitate real-time data exploration. The study also investigates the usability of these tools through expert-based evaluations, aiming to assess their effectiveness in supporting informed and timely decision-making. The findings reveal that interactive visualizations significantly improve insight discovery and pattern recognition, enabling decision-makers to uncover hidden trends in large multimedia datasets. However, challenges related to scalability, user acceptance, and real-time processing were encountered during the implementation phase. The research highlights the practical benefits of integrating visual analytics into industry workflows, which include enhanced content creation, audience engagement, and strategic planning. Furthermore, the study identifies key visual analytics techniques such as dynamic dashboards, pattern recognition, data mining, and clustering, which are essential for analyzing multimedia data. The study concludes by emphasizing the potential for wider applications of visual analytics in other sectors, suggesting future research directions to improve tool performance, scalability, and user accessibility, as well as exploring the integration of emerging technologies like artificial intelligence and virtual reality.

The analysis methods used in this study include a case study on the use of closed-loop recycling and an evaluation of biopolymer performance across various industries, both of which are important components in the transformation of the manufacturing industry toward a circular economy. The research findings indicate that recycled materials can reduce carbon emissions by thirty to fifty percent and save production costs by fifteen to twenty-five percent. Artificial intelligence-based sorting technology improves sorting efficiency to 95 percent, and closed-loop recycling maintains the mechanical properties of materials up to 90 percent after four cycles. The degradation rate of biopolymers like PLA and PHA reaches 60-80% within six months, although production costs are still 2-3 times higher. The integrated approach increases resource efficiency by 45% and reduces waste by 60%. To achieve successful implementation, Extended Producer Responsibility (EPR) policies, strategic infrastructure investments, and collaboration from various parties thru the triple helix model must work together.

Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.

Explainable Artificial Intelligence (XAI) has become a critical area of research within artificial intelligence, focusing on improving the transparency and interpretability of machine learning (ML) models, often referred to as "black-box" models. The need for XAI techniques arises from the inherent complexity of ML models, which can make their decision-making processes difficult for users to understand. This study investigates various XAI techniques, including LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), to assess their impact on model interpretability without significantly compromising predictive performance. A comparative experimental design was used, applying these XAI methods to different ML models, including deep neural networks and ensemble methods, within large-scale enterprise data analytics systems. The results indicate that XAI methods significantly enhance model transparency and decision traceability, allowing users to understand the influence of individual features on predictions. While a slight reduction in predictive accuracy was observed, especially with simpler models, the trade-off between interpretability and performance was deemed acceptable, particularly in fields requiring transparency, such as healthcare, finance, and autonomous systems. The use of XAI in enterprise data systems has practical implications for fostering trust and enabling informed decision-making among stakeholders. Furthermore, the study discusses the challenges and limitations of applying XAI techniques, such as complexity, scalability, and model-specific limitations. Future research is suggested to focus on developing more scalable and efficient XAI methods, enhancing their applicability across various model types, and addressing the challenges of real-time applications. This will be crucial in ensuring the widespread adoption of XAI in critical domains, promoting the ethical use of AI while maintaining predictive accuracy.

This research explores the integration of Enterprise Architecture (EA) and Artificial Intelligence (AI) to optimize strategic decision-making in digital service-oriented organizations. These organizations often face challenges such as fragmented decision-making due to disconnected IT systems and limited data-driven insights. The objective of the study is to develop an integrated framework that combines EA and AI to enhance decision-making accuracy, operational efficiency, and strategic alignment. The study employs design science research methodology, involving the development of the framework, expert validation, and testing in simulated organizational scenarios. The findings reveal that the integrated framework improves decision-making by providing real-time, data-driven insights, predictive analytics, and better alignment with organizational goals. AI's role in analyzing large datasets and generating actionable insights allows decision-makers to anticipate future trends and make more informed decisions. The framework significantly outperforms traditional EA approaches, particularly in terms of predictive decision support and adaptive intelligence. The study concludes that the integration of EA and AI provides a robust solution for organizations looking to improve strategic decision-making, enhance operational efficiency, and stay competitive in dynamic business environments.